Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f331ac39898>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f331aad85c0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
  return f(*args, **kwds)
TensorFlow Version: 1.4.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, (None), name='learning_rate')
    
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [409]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    print(images)
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28xnum_channels
        x1 = tf.layers.conv2d(images, 128, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha*x1, x1)
        # 14x14x128
        
        x2 = tf.layers.conv2d(relu1, 256, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha*bn2, bn2)
        # 7x7x256
        
        x3 = tf.layers.conv2d(relu2, 512, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha*bn3, bn3)
        # 4x4x512
        
        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*512))
        logits = tf.layers.dense(flat, 1)
        
        # Dropouts not needed since batch normalization is done
        # logits = tf.layers.dropout(logits, rate=0.5)
        
        output = tf.sigmoid(logits)

    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tensor("Placeholder:0", shape=(?, 28, 28, 3), dtype=float32)
Tensor("Placeholder:0", shape=(?, 28, 28, 3), dtype=float32)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [406]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha*x1, x1)
        # 7x7x512 now
 
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha*x2, x2)
        # 14x14x256 now
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha*x3, x3)
        # 28x28x128 now

        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 2, strides=1, padding='same')
        # 28x28xout_channel_dim now

        output = tf.tanh(logits)
           
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [396]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
    
    # Added smoothing to labels to help the discriminator generalize better
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * 0.9))
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tensor("Placeholder:0", shape=(?, 28, 28, 4), dtype=float32)
Tensor("generator/Tanh:0", shape=(?, 28, 28, 4), dtype=float32)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [397]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [398]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [402]:
def train(epoch_count, batch_size, z_dim, learn_rate, beta1, alpha, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
        
    input_real, input_z, learning_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], alpha=alpha)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)

    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                # Since input images are scaled to [-0.5, 0.5]  
                batch_images *= 2

                steps += 1

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, 
                                               learning_rate: learn_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, 
                                               learning_rate: learn_rate})
                # Running generator optimization one more time to help prevent descriminator loss from going to 0
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, 
                                               learning_rate: learn_rate})

                if steps % 100 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    show_generator_output(sess, 32, input_z, data_shape[3], data_image_mode)                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [411]:
batch_size = 32
z_dim = 100
learning_rate = 0.0005
alpha = 0.2
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""

epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, alpha, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Tensor("input_real:0", shape=(?, 28, 28, 1), dtype=float32)
Tensor("generator/Tanh:0", shape=(?, 28, 28, 1), dtype=float32)
Epoch 1/2... Discriminator Loss: 0.6033... Generator Loss: 1.7486
Epoch 1/2... Discriminator Loss: 1.1827... Generator Loss: 1.0653
Epoch 1/2... Discriminator Loss: 0.8559... Generator Loss: 1.5855
Epoch 1/2... Discriminator Loss: 1.1546... Generator Loss: 0.7508
Epoch 1/2... Discriminator Loss: 1.4848... Generator Loss: 0.4996
Epoch 1/2... Discriminator Loss: 1.1125... Generator Loss: 1.0938
Epoch 1/2... Discriminator Loss: 1.1040... Generator Loss: 1.0376
Epoch 1/2... Discriminator Loss: 0.9958... Generator Loss: 1.0233
Epoch 1/2... Discriminator Loss: 1.2863... Generator Loss: 2.1524
Epoch 1/2... Discriminator Loss: 1.0592... Generator Loss: 1.1558
Epoch 1/2... Discriminator Loss: 1.0670... Generator Loss: 1.3755
Epoch 1/2... Discriminator Loss: 1.0649... Generator Loss: 1.0776
Epoch 1/2... Discriminator Loss: 1.0212... Generator Loss: 1.2301
Epoch 1/2... Discriminator Loss: 1.0456... Generator Loss: 1.4873
Epoch 1/2... Discriminator Loss: 1.2949... Generator Loss: 0.6152
Epoch 1/2... Discriminator Loss: 0.9427... Generator Loss: 1.3395
Epoch 1/2... Discriminator Loss: 1.0636... Generator Loss: 0.7909
Epoch 1/2... Discriminator Loss: 0.8141... Generator Loss: 1.2785
Epoch 2/2... Discriminator Loss: 1.3254... Generator Loss: 0.6425
Epoch 2/2... Discriminator Loss: 1.0500... Generator Loss: 0.9120
Epoch 2/2... Discriminator Loss: 1.3778... Generator Loss: 0.6779
Epoch 2/2... Discriminator Loss: 0.8255... Generator Loss: 1.3202
Epoch 2/2... Discriminator Loss: 1.9495... Generator Loss: 0.3166
Epoch 2/2... Discriminator Loss: 0.7027... Generator Loss: 1.6080
Epoch 2/2... Discriminator Loss: 1.2038... Generator Loss: 0.8519
Epoch 2/2... Discriminator Loss: 1.2247... Generator Loss: 0.7738
Epoch 2/2... Discriminator Loss: 0.8821... Generator Loss: 1.1783
Epoch 2/2... Discriminator Loss: 2.0700... Generator Loss: 0.3041
Epoch 2/2... Discriminator Loss: 0.7925... Generator Loss: 1.4699
Epoch 2/2... Discriminator Loss: 1.1781... Generator Loss: 1.0112
Epoch 2/2... Discriminator Loss: 0.7967... Generator Loss: 2.4844
Epoch 2/2... Discriminator Loss: 1.5548... Generator Loss: 0.4651
Epoch 2/2... Discriminator Loss: 0.9747... Generator Loss: 1.1980
Epoch 2/2... Discriminator Loss: 0.8093... Generator Loss: 1.3330
Epoch 2/2... Discriminator Loss: 1.0643... Generator Loss: 0.9582
Epoch 2/2... Discriminator Loss: 0.7904... Generator Loss: 1.3143
Epoch 2/2... Discriminator Loss: 0.8160... Generator Loss: 1.2612

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [410]:
batch_size = 32
z_dim = 100
learning_rate = 0.0005
alpha = 0.2
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, alpha, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Tensor("input_real:0", shape=(?, 28, 28, 3), dtype=float32)
Tensor("generator/Tanh:0", shape=(?, 28, 28, 3), dtype=float32)
Epoch 1/1... Discriminator Loss: 0.3483... Generator Loss: 5.0551
Epoch 1/1... Discriminator Loss: 0.3631... Generator Loss: 5.2215
Epoch 1/1... Discriminator Loss: 1.6112... Generator Loss: 0.5991
Epoch 1/1... Discriminator Loss: 1.2498... Generator Loss: 0.8780
Epoch 1/1... Discriminator Loss: 1.4010... Generator Loss: 0.8518
Epoch 1/1... Discriminator Loss: 1.0904... Generator Loss: 1.1427
Epoch 1/1... Discriminator Loss: 1.3034... Generator Loss: 0.7507
Epoch 1/1... Discriminator Loss: 1.0922... Generator Loss: 1.9733
Epoch 1/1... Discriminator Loss: 1.6152... Generator Loss: 0.4386
Epoch 1/1... Discriminator Loss: 1.1137... Generator Loss: 1.0533
Epoch 1/1... Discriminator Loss: 0.9212... Generator Loss: 1.8855
Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.8648
Epoch 1/1... Discriminator Loss: 0.9228... Generator Loss: 1.1685
Epoch 1/1... Discriminator Loss: 1.4096... Generator Loss: 0.5345
Epoch 1/1... Discriminator Loss: 1.3094... Generator Loss: 0.7234
Epoch 1/1... Discriminator Loss: 1.0496... Generator Loss: 0.8349
Epoch 1/1... Discriminator Loss: 1.4470... Generator Loss: 0.7592
Epoch 1/1... Discriminator Loss: 1.2609... Generator Loss: 0.8768
Epoch 1/1... Discriminator Loss: 1.3371... Generator Loss: 0.8339
Epoch 1/1... Discriminator Loss: 1.3796... Generator Loss: 0.9802
Epoch 1/1... Discriminator Loss: 0.8897... Generator Loss: 1.3997
Epoch 1/1... Discriminator Loss: 1.2440... Generator Loss: 0.6537
Epoch 1/1... Discriminator Loss: 1.4160... Generator Loss: 0.8187
Epoch 1/1... Discriminator Loss: 1.5264... Generator Loss: 0.4607
Epoch 1/1... Discriminator Loss: 1.1157... Generator Loss: 0.6673
Epoch 1/1... Discriminator Loss: 1.3662... Generator Loss: 0.5814
Epoch 1/1... Discriminator Loss: 1.5870... Generator Loss: 0.4195
Epoch 1/1... Discriminator Loss: 1.2981... Generator Loss: 0.8125
Epoch 1/1... Discriminator Loss: 0.8972... Generator Loss: 2.7068
Epoch 1/1... Discriminator Loss: 2.7720... Generator Loss: 2.8905
Epoch 1/1... Discriminator Loss: 1.2704... Generator Loss: 1.1593
Epoch 1/1... Discriminator Loss: 1.7112... Generator Loss: 0.3614
Epoch 1/1... Discriminator Loss: 1.3602... Generator Loss: 0.6253
Epoch 1/1... Discriminator Loss: 1.2552... Generator Loss: 0.7724
Epoch 1/1... Discriminator Loss: 0.6849... Generator Loss: 1.4581
Epoch 1/1... Discriminator Loss: 1.3059... Generator Loss: 0.8204
Epoch 1/1... Discriminator Loss: 1.0574... Generator Loss: 0.9263
Epoch 1/1... Discriminator Loss: 1.5793... Generator Loss: 1.6152
Epoch 1/1... Discriminator Loss: 1.4532... Generator Loss: 0.4721
Epoch 1/1... Discriminator Loss: 1.1893... Generator Loss: 0.7775
Epoch 1/1... Discriminator Loss: 1.2960... Generator Loss: 1.5509
Epoch 1/1... Discriminator Loss: 1.4488... Generator Loss: 1.6902
Epoch 1/1... Discriminator Loss: 1.2620... Generator Loss: 0.8495
Epoch 1/1... Discriminator Loss: 1.1378... Generator Loss: 0.7428
Epoch 1/1... Discriminator Loss: 0.7353... Generator Loss: 1.2490
Epoch 1/1... Discriminator Loss: 1.4325... Generator Loss: 0.8255
Epoch 1/1... Discriminator Loss: 1.2784... Generator Loss: 2.2806
Epoch 1/1... Discriminator Loss: 0.7697... Generator Loss: 2.4821
Epoch 1/1... Discriminator Loss: 1.9471... Generator Loss: 2.2039
Epoch 1/1... Discriminator Loss: 1.2217... Generator Loss: 0.6724
Epoch 1/1... Discriminator Loss: 0.6752... Generator Loss: 2.9774
Epoch 1/1... Discriminator Loss: 0.8738... Generator Loss: 1.0873
Epoch 1/1... Discriminator Loss: 0.8970... Generator Loss: 1.2773
Epoch 1/1... Discriminator Loss: 1.1556... Generator Loss: 0.6758
Epoch 1/1... Discriminator Loss: 1.1416... Generator Loss: 1.2875
Epoch 1/1... Discriminator Loss: 1.1135... Generator Loss: 0.9894
Epoch 1/1... Discriminator Loss: 1.1630... Generator Loss: 0.9062
Epoch 1/1... Discriminator Loss: 1.1539... Generator Loss: 0.8282
Epoch 1/1... Discriminator Loss: 1.1383... Generator Loss: 0.9574
Epoch 1/1... Discriminator Loss: 1.3830... Generator Loss: 0.5945
Epoch 1/1... Discriminator Loss: 1.3562... Generator Loss: 1.9582
Epoch 1/1... Discriminator Loss: 1.2867... Generator Loss: 0.9483
Epoch 1/1... Discriminator Loss: 1.2876... Generator Loss: 1.0580

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.